14 citations · 15 across the 2 of their papers we have counts for
4 papers
Proposal Flow: Semantic Correspondences from Object Proposals
Bumsub Ham, Minsu Cho, Cordelia Schmid +1
Finding image correspondences remains a challenging problem in the presence of intra-class variations and large changes in scene layout. Semantic flow methods are designed to handl…
FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence
Seungryong Kim, Dongbo Min, Bumsub Ham +3
We present a descriptor, called fully convolutional self-similarity (FCSS), for dense semantic correspondence. To robustly match points among different instances within the same ob…
DASC: Robust Dense Descriptor for Multi-modal and Multi-spectral Correspondence Estimation
Seungryong Kim, Dongbo Min, Bumsub Ham +2
Establishing dense correspondences between multiple images is a fundamental task in many applications. However, finding a reliable correspondence in multi-modal or multi-spectral i…
Efficient Splitting-based Method for Global Image Smoothing
Youngjung Kim, Dongbo Min, Bumsub Ham +1
Edge-preserving smoothing (EPS) can be formulated as minimizing an objective function that consists of data and prior terms. This global EPS approach shows better smoothing perform…